This disclosure relates generally to decision making, and particularly to eliciting a minimal number of questions to a decision maker in order to solve a decision problem.
An influence diagram is a directed and acyclic graph for use in decision making under uncertainty. The influence diagram includes chance or random variables which specify the uncertain decision environment, decision variables which specify possible decisions to be made in a corresponding decision problem, and a utility function which represents preferences of a decision maker. Each chance variable is associated with a parent set (possibly empty) in a graph which together with that chance variable define a conditional probability distribution. A product of conditional probability distributions defines a joint probability distribution over all possible outcomes in the decision problem. Each decision variable has a parent set (possibly empty) including one or more variables whose values will be known at the time of making of corresponding decisions and may affect directly the decisions. The decision variables are typically assumed to be temporally ordered. A strategy or policy for an influence diagram is a list of decision rules including one rule for each decision variable specifying which decision to make for each value instantiation of the variables in its parent set. Solving an influence diagram is to find an optimal policy that maximizes an expected utility, i.e., achieves a goal of the decision maker.
A probabilistic decision tree (PDT) refers to a model of a decision problem that represents all choices, outcomes and paths that a decision maker may have. A main objective of building and solving a PDT is to find choices that satisfy the decision maker's situation and preference.
As an example, currently, in a health care domain, a patient considers attributes of treatment options before deciding which treatment option to undertake. Attributes include, but are not limited to: pain, disability, side effects, resulting state after a treatment, cost of a treatment, life expectancy. In this multi-attribute decision making, the patient is required to fully elicit his/her preference among all the attributes. The full elicitation from the patient can be time-consuming and cognitively difficult.